{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1、 导入必要的工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#color = sns.color_palette()\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2、查看前5个样本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('diabetes.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3、打印每个特征的特性，平均值、方差、分位数和最大最小值等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据常识和经验。下面几个特征是不会出现0值的。\n",
    "    Glucose：  口服葡萄糖耐受试验中，2 小时的血浆葡萄糖浓度。\n",
    "    BloodPressure：  舒张压（mm  Hg）\n",
    "    SkinThickness：  三头肌皮肤褶层厚度（mm ）\n",
    "    Insulin ：2 小时血清胰岛素含量（μU/  ml）\n",
    "    BMI：  体重指数（体重，kg /（身高，m ）^ 2）\n",
    "所以需要对0值填充。这里使用平均值填充"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4、数据探索\n",
    "4.1 柱形看出，样本中不得病比得病要多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,u'Number of occurrences')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb017908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train.Outcome)\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.2 随着怀孕的次数增加，得病的可能性增大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb048a90>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb09d828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Pregnancies\",hue=\"Outcome\",data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.3 发现葡萄糖浓度跟得病的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb3ef358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome',y='Glucose',data=train)\n",
    "plt.xlabel('Diabetes',fontsize=12)\n",
    "plt.ylabel('Number of occurrences',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.4 随着年龄的增大得病的概率比不得病的概率要高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb5b9588>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb18d160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DF = train.groupby(['Age','Outcome'])['Age'].count().unstack('Outcome')\n",
    "DF[[0,1]].plot(kind='bar',stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.5 除了怀孕次数和年龄的相关性大于0.5外。没有其他的两个特性大于0.5的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc40d048>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc41e550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr = train.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(20,10))\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5.数据填补"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "NaN_col_names = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI']\n",
    "train[NaN_col_names] = train[NaN_col_names].replace(0,np.NaN)\n",
    "medians = train.median()\n",
    "train = train.fillna(medians)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.to_csv('FE_Diabetes.csv',index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
